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      Validity of age estimation methods and reproducibility of bone/dental maturity indices for chronological age estimation: a systematic review and meta-analysis of validation studies

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          Abstract

          Several approaches have been developed to estimate age, an important aspect of forensics and orthodontics, using different measures and radiological examinations. Here, through meta-analysis, we determined the validity of age estimation methods and reproducibility of bone/dental maturity indices used for age estimation. The PubMed and Google Scholar databases were searched to December 31, 2021 for human cross-sectional studies meeting pre-defined PICOS criteria that simultaneously assessed the reproducibility and validity. Meta-estimates of validity (mean error: estimated age-chronological age) and intra- and inter-observer reproducibility (Cohen’s kappa, intraclass correlation coefficient) and their predictive intervals (PI) were calculated using mixed-effect models when heterogeneity was high (I 2 > 50%). The literature search identified 433 studies, and 23 met the inclusion criteria. The mean error meta-estimate (mixed effects model) was 0.08 years (95% CI − 0.12; 0.29) in males and 0.09 (95% CI − 0.12; 0.30) in females. The PI of each method spanned zero; of nine reported estimation methods, Cameriere’s had the smallest (− 0.82; 0.47) and Haavikko’s the largest (− 7.24; 4.57) PI. The reproducibility meta-estimate (fixed effects model) was 0.98 (95% CI 0.97; 1.00) for intra- and 0.99 (95% CI 0.98; 1.00) for inter-observer agreement. All methods were valid but with different levels of precision. The intra- and inter-observer reproducibility was high and homogeneous across studies.

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          QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies.

          In 2003, the QUADAS tool for systematic reviews of diagnostic accuracy studies was developed. Experience, anecdotal reports, and feedback suggested areas for improvement; therefore, QUADAS-2 was developed. This tool comprises 4 domains: patient selection, index test, reference standard, and flow and timing. Each domain is assessed in terms of risk of bias, and the first 3 domains are also assessed in terms of concerns regarding applicability. Signalling questions are included to help judge risk of bias. The QUADAS-2 tool is applied in 4 phases: summarize the review question, tailor the tool and produce review-specific guidance, construct a flow diagram for the primary study, and judge bias and applicability. This tool will allow for more transparent rating of bias and applicability of primary diagnostic accuracy studies.
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            Plea for routinely presenting prediction intervals in meta-analysis

            Objectives Evaluating the variation in the strength of the effect across studies is a key feature of meta-analyses. This variability is reflected by measures like τ2 or I2, but their clinical interpretation is not straightforward. A prediction interval is less complicated: it presents the expected range of true effects in similar studies. We aimed to show the advantages of having the prediction interval routinely reported in meta-analyses. Design We show how the prediction interval can help understand the uncertainty about whether an intervention works or not. To evaluate the implications of using this interval to interpret the results, we selected the first meta-analysis per intervention review of the Cochrane Database of Systematic Reviews Issues 2009–2013 with a dichotomous (n=2009) or continuous (n=1254) outcome, and generated 95% prediction intervals for them. Results In 72.4% of 479 statistically significant (random-effects p 0), the 95% prediction interval suggested that the intervention effect could be null or even be in the opposite direction. In 20.3% of those 479 meta-analyses, the prediction interval showed that the effect could be completely opposite to the point estimate of the meta-analysis. We demonstrate also how the prediction interval can be used to calculate the probability that a new trial will show a negative effect and to improve the calculations of the power of a new trial. Conclusions The prediction interval reflects the variation in treatment effects over different settings, including what effect is to be expected in future patients, such as the patients that a clinician is interested to treat. Prediction intervals should be routinely reported to allow more informative inferences in meta-analyses.
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              A new system of dental age assessment.

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                Author and article information

                Contributors
                e.skrami@staff.univpm.it
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                16 September 2022
                16 September 2022
                2022
                : 12
                : 15607
                Affiliations
                [1 ]GRID grid.7010.6, ISNI 0000 0001 1017 3210, Postgraduate School of Medical Statistics and Biometry, Department of Biomedical Sciences and Public Health, , Università Politecnica delle Marche, ; 60126 Ancona, Italy
                [2 ]GRID grid.7010.6, ISNI 0000 0001 1017 3210, Center of Epidemiology, Biostatistics and Medical Information Technology, , Università Politecnica delle Marche, ; 60126 Ancona, Italy
                [3 ]GRID grid.7010.6, ISNI 0000 0001 1017 3210, Department of Biomedical Sciences and Public Health, , Università Politecnica delle Marche, ; 60126 Ancona, Italy
                [4 ]GRID grid.7010.6, ISNI 0000 0001 1017 3210, Department of Pediatrics, , Università Politecnica delle Marche, ; 60123 Ancona, Italy
                [5 ]GRID grid.4708.b, ISNI 0000 0004 1757 2822, National Centre for Healthcare Research and Pharmacoepidemiology, ; 20126 Milan, Italy
                Article
                19944
                10.1038/s41598-022-19944-5
                9481543
                36114349
                f09f30f2-8f53-4c93-a49a-90d00119578a
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 19 May 2022
                : 6 September 2022
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                medical research,statistics
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